Limiting blockchain size to optimize performance

ABSTRACT

A blockchain may include various transactions which are identified and which require processing. The order of processing such transactions may be optimized by examining content of the transactions. One example operation may include one or more of identifying blockchain transactions, designating each of the blockchain transactions as an independent transaction type or a dependent transaction type, and determining an order to process the blockchain transactions based on the independent transaction type or the dependent transaction type.

TECHNICAL FIELD

This application relates to performance optimization and, moreparticularly, to limiting the size of a blockchain to optimizeperformance.

BACKGROUND

One feature of a blockchain is that it records each transaction in anopen shared ledger. Unfortunately, this is sometimes a problem sinceeach and every aspect of the transactions are recorded on a blockchainand thus the size of the blockchain in memory can become very large.Updating a blockchain is linearly proportional to its size. Thus, foreach transaction, if the problem size is O(n) for a ‘n’ length blockchain, the problem becomes O(n̂2). The size problem can grow based on aquadratic expression representing the size of the block chain. The sizebecomes enormously long very quickly in a domain of the Internet ofThings (IoT) where all transactions occur between different machines.Machines can create transactional events at an enormous speed on theorder of 10̂6 or more each second, based on the complexity of the systemand the number of IoT devices. Since the blockchain is an open ledgerthat is updated and shared among all IoT devices, the length of theblockchain can adversely impact the performance of an IoT type ofsystem.

Managing the length of a blockchain is a major issue for anyimplementation and application of blockchains whether it is apermission-less ledger or a needs-permission ledger. The problem is moreprevalent to permission-less ledgers where the size is not under strictcontrol.

SUMMARY

One example embodiment may include a method that comprises one or moreof identifying a frequency of access of one or more portions of ablockchain, determining the one or more portions of the blockchain whichare eligible for archiving based on the frequency of access of the oneor more portions of the blockchain, and compressing and archiving theone or more portions of the blockchain eligible for archiving.

Another example embodiment may include an apparatus comprising aprocessor configured to perform one or more of identify a frequency ofaccess of one or more portions of a blockchain, determine the one ormore portions of the blockchain which are eligible for archiving basedon the frequency of access of the one or more portions of theblockchain, and compress and archive the one or more portions of theblockchain eligible for archiving.

Still another example embodiment may include a non-transitory computerreadable storage medium configured to store instructions that whenexecuted cause a processor to perform one or more of identifying afrequency of access of one or more portions of a blockchain, determiningthe one or more portions of the blockchain which are eligible forarchiving based on the frequency of access of the one or more portionsof the blockchain, and compressing and archiving the one or moreportions of the blockchain eligible for archiving.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a logic block diagram of a blockchain transactiontracking configuration according to example embodiments.

FIG. 2 illustrates a system signaling diagram of a blockchaintransaction tracking configuration according to example embodiments.

FIG. 3A illustrates a flow diagram of an example method of operationaccording to example embodiments.

FIG. 3B illustrates a flow diagram of an example method of operationaccording to example embodiments.

FIG. 4 illustrates an example network entity configured to support oneor more of the example embodiments.

DETAILED DESCRIPTION

It will be readily understood that the instant components, as generallydescribed and illustrated in the figures herein, may be arranged anddesigned in a wide variety of different configurations. Thus, thefollowing detailed description of the embodiments of at least one of amethod, apparatus, non-transitory computer readable medium and system,as represented in the attached figures, is not intended to limit thescope of the application as claimed, but is merely representative ofselected embodiments.

The instant features, structures, or characteristics as describedthroughout this specification may be combined in any suitable manner inone or more embodiments. For example, the usage of the phrases “exampleembodiments”, “some embodiments”, or other similar language, throughoutthis specification refers to the fact that a particular feature,structure, or characteristic described in connection with the embodimentmay be included in at least one embodiment. Thus, appearances of thephrases “example embodiments”, “in some embodiments”, “in otherembodiments”, or other similar language, throughout this specificationdo not necessarily all refer to the same group of embodiments, and thedescribed features, structures, or characteristics may be combined inany suitable manner in one or more embodiments.

In addition, while the term “message” may have been used in thedescription of embodiments, the application may be applied to many typesof network data, such as, packet, frame, datagram, etc. The term“message” also includes packet, frame, datagram, and any equivalentsthereof. Furthermore, while certain types of messages and signaling maybe depicted in exemplary embodiments they are not limited to a certaintype of message, and the application is not limited to a certain type ofsignaling.

In one embodiment, blockchain management actions are taken when ablockchain size has exceeded a predetermined size. Both permission-lessledgers as well as permission-required ledgers may both utilize theblockchain optimization procedure described in the example embodiments.In an example, each block of a blockchain can be up to a certain size,such as 1 MB, but the size is still not fixed. Also, each block in suchan example of blockchain, may contain a large number of transactions,such as 2,400 transactions). As transaction blocks are supposed to befound in various time frames, for example every ten minutes, this wouldyield 14,400 transactions per hour or four transactions per second. Ifmore transactions are received they would compete for inclusion into theblocks. Transactions with smaller fees would gain priority by aging andwould at some point be included at times of fewer transactions.Requirements of transactions arriving quickly would attach biggertransaction fees for faster inclusion. At some point, micro-transactionswould cost more than the amount of value they represent and/or wouldhave to be logged for long periods of time.

The current blockchain protocol would not be able to sustain atransaction volume of millions of transactions per minute. Many creditcard companies process 150 million transactions per day, which equalsabout 100,000 transactions per minute. Some crypto-currency networks areaveraging approximately 60,000 transactions per day, or about ⅙ of whatthe network can handle with the current block size limit. These networkscould scale to more than 40,000 transactions per second. In such a case,the common users would probably not store the complete blockchainanymore, but only the unspent outputs or some other derivative withoutthe entire history of the transactions. Clients do not store the entireblockchain. There are going to be historical transactions that do nothave any coins or value associated with them. This would indicate thatclients could purge a large portion of the database used to store theblockchain.

Example embodiments provide limiting blockchain length by selectivelycompressing and archiving selected portions of the blockchain in orderto increase and maintain computational efficiency, maintain alltransactions while keeping the length of the blockchain fixed andlimiting its growth by determining least used sections of the blockchainand archiving those sections for later use, such that archived portionsmay be accessed and brought back in the original ledger if necessary.

FIG. 1 illustrates a logic block diagram of a blockchain transactiontracking configuration according to example embodiments. Referring toFIG. 1, the logic diagram 100 includes receiving blockchain transactions110, and storing the transactions in appropriate blocks 122, 124, 126,etc. The transactions are logged and stored in a logging procedureassociated with blockchain protocols. However, as the overall size ofthe blockchain grows, a threshold size may be reached at which point thetransactions are organized and audited for certain transactionattributes, such as when the transaction was received and logged, howlong the transaction has been stored in the blockchain, a time when thetransaction was last accessed, how many times the transaction has beenaccessed over a period of time, etc. One or more transaction attributesmay be used as the basis for selection, removal and archiving in memory.

The transactions may be compressed as they are removed from theoptimized blockchain 150 and replaced with transaction metadata, such assource information, destination information, monetary information, etc.,and other key attributes which are preserved to reference thetransactions after they are removed and compressed. The compressedtransactions 130 are then placed in an archive 140, such as a securedatabase for fast reference at a later time. The optimized blockchain150 may have a record of all transactions, however, not all transactionsmay be stored in the optimized blockchain 150 since some have beenremoved and archived.

The size of a blockchain block depends on the standardization being usedwith that particular blockchain. The threshold size used to decide whento perform an archive operation can also be decided based on the speedor rate at which transactions are being accumulated. When thetransaction/block growth rate slows beyond a speed at which thetransaction is generated or created it may be time to initiate anarchive operation otherwise the buffers may be full rather quickly. Thearchiving should be performed before an overfill condition occurs.

Archiving transactions which are related to a blockchain client that ispart of the blockchain network may occur if the clientdis-enrolls/deregisters from the blockchain network. The archivedtransactions may be brought back in the active ledger if the clientjoins back and re-registers with the network. Archiving least frequentlyused transactions may be performed responsive to when the blockchainreaches its limiting size. The transactions may be brought back in theledger if needed at a later time. The examples disclosed will maintainall the transactions while keeping the length of the blockchain fixed.In general, the running length of the blockchain should be fixed. Thathelps estimate the size and the time required for each transaction. Thesize of the blockchain depends on the current standard (for example, 1MB), however the size may grow as the technology changes for fasterprocessors and larger memory. It is optimal to compress any transactionthat is archived for longer storage.

FIG. 2 illustrates a system signaling diagram of a blockchaintransaction tracking configuration according to example embodiments.Referring to FIG. 2, the system 200 includes a blockchain client 210which operates as an independent entity seeking access to the blockchainto observe transactions, write transactions, etc. The optimizedblockchain 220, which may be stored on the client 210 or anothercomputer, may be the same as a regular blockchain. However, sometransactions are normal and some are merely placeholders of actualtransactions which are archived and compressed 230. In operation, theblockchain client 210 may forward transactions 222 to the blockchain 220for storage in a blockchain block. The transactions may be queued 224based on the time they arrive, and based on the frequency 226 of accessby third parties, etc. The least accessed transactions and/or the oldesttransactions may be stored at the top of the queue for easy selectionand removal during a compression/archive operation.

The transaction access attempts may be logged and identified for eachtransaction. As the transactions are accessed the relative importance ofthe transaction increases. For example, if a transaction was accessedmultiple times, each time it was accessed may have been recorded andused to weight the transaction as having a relative level of importance.An importance level transaction attribute that is higher than othertransactions may be less likely to be archived. Also, a more recentaccess may take priority over a less recent access operation. Forexample, a transaction that was accessed in a current day may be movedfurther down the queue of transactions to ensure the transaction is notarchived prematurely. Once one or more transactions are identified forarchiving 228, the transactions may be archived 232 to a database toreduce the size of the optimized blockchain 220. The blockchain may beupdated 234 to reflect the change(s). The optimized blockchain 220 canbe accessed for retrieving various transactions 236 via the blockchainclient 210. If a transaction is accessed that has been archived, then itmay be retrieved from memory 230 and restored if necessary.

When an archived portion of the blockchain (i.e., one or moretransactions) is needed, that portion is accessed and brought back tothe original ledger. In one example, each transaction is maintained witha time stamp. The number of times each transaction is viewed, read, orwritten is maintained as an attribute to the transaction indicating itsaccess frequency. Transactions are sorted in a priority queue so thatthe least frequent transactions are at the top of the queue, in oneembodiment. When the blockchain reaches its limiting size then one ormore of the least frequent transactions are archived. The archive can bestored in an encrypted loss-less compressed format and can be accessedwhenever needed.

In another example, a group of neighboring sections of severaltransactions may be grouped together and moved-off the blockchain (i.e.,compressed and archived). The size of the section can be fixed or varieddepending on the demand and the age and usage of the transaction chainsand where the truncated bits are store. For example, a group of 10 ormore transactions may be identified as being from a seldom referencedsource and may be grouped together as a candidate for compression andarchiving. Another example is when one transaction initiated anothertransaction which in turn may have initiated one or more transactionsand each transaction, in turn, may have initiated others. That set oftransactions can all be related and grouped together. However, aftersome time these transactions may not have a need of use or view withinthe purview of the blockchain and may therefore be archived togetheruntil they are all needed together back again and then they can all bede-archived. The grouping of transactions can be decided by manyfactors. One example is the geo-location of the transaction orrelationship to a IoT devices.

In an alternative embodiment, the frequency of access attempts is usedas a linear function based on the viewed, read, and writtentransactions, where each attribute is assigned a weight based on itssignificance within the blockchain that can be adjusted to make theblock chain more efficient. For example, each time a transaction isviewed or read, the weight may be applied to increase the transaction'scurrent status. Moving the transaction into a different position of thequeue may be performed to reflect the change in the transaction's weightand status. For example, a transaction may be located at a higher partof a queue and when it is accessed it may move down the queue based onits applied attribute/weight. If that same transaction is againaccessed, then it may be moved down even further by an amountcommensurate with the weight.

In another embodiment, several related transactions may be archivedtogether. The relationship between the transactions can be decidedeither by determining their connectivity such as origin or messagepassing to a group of IoT devices clustered together and/or networkedtogether and based on their event timings. When one such transaction istaken and selected for archiving, all other related transactions arealso taken out preemptively to reduce the archiving time. At the sametime when one such transaction is taken out of the archive, all otherrelated transactions are also taken out preemptively to reduce thede-archiving time.

Another example embodiment may include a least frequently usedtransaction or its related transactions can be archived based on thosewhich are least commonly accessed. For example, a least commonly usedtransaction is determined by how many distinct IoT devices have accessedthe transaction.

Yet in another embodiment, the frequencies of access and similar actionswith regard to transactions stored in the blockchain may be identifiedand organized based on different groups. The groups may be furthergrouped based on the group access patterns. The access patterns areidentified using a cognitive learning algorithm, where characteristicsare learned and identified and stored in an access file. The blockchaincan use pointers to access the transactions accordingly and based on thecharacteristics. Also, transactions related to a blockchain client whichare part of the blockchain network could be archived if the client isidentified as having unenrolled/deregistered from the blockchainnetwork. When the client joins back and re-registers with the network,transactions could be brought back in the active ledger from thearchive.

FIG. 3A illustrates a flow diagram of an example method of operationaccording to example embodiments. Referring to FIG. 3A, the method 300may include identifying a frequency of access of one or more portions ofa blockchain 312, determining the one or more portions of the blockchainwhich are eligible for archiving based on the frequency of access of theone or more portions of the blockchain 314 and compressing and archivingthe one or more portions of the blockchain eligible for archiving 316.The blocks/transactions/portions of the blockchain which are eligiblefor archiving may not be accessed frequently or at all. The importanceof a transaction or other portion of the blockchain may render thetransaction active and ineligible for compression and archiving.

The method may also include determining a maximum data size threshold ofthe blockchain, identifying a client profile has withdrawn enrollmentfrom the blockchain, identifying a plurality of client profiletransactions logged in the blockchain corresponding to the clientprofile, and archiving the plurality of client profile transactionscorresponding to the client profile. The method may also includeidentifying the client profile has re-enrolled with the blockchain,identifying the plurality of client profile transactions correspondingto the client profile, and restoring the plurality of client profiletransactions responsive to the client profile being re-enrolled with theblockchain. The method may further include determining the blockchaindata stored in the blockchain has reached the maximum data sizethreshold, and responsive to determining the blockchain data has reachedthe maximum data size threshold, archiving least frequently accessedblockchain transactions. The method may also include storing blockchaintransactions of the blockchain in a priority queue with the leastfrequently used transactions at respective topmost positions of thequeue, and when the blockchain reaches the maximum data size threshold,archiving the least frequently accessed blockchain transactions at therespective topmost positions of the queue.

FIG. 3B illustrates another flow diagram 350 of another example methodof operation according to example embodiments. The method may includeidentifying one or more portions of a blockchain transaction requiringcompression 352, compressing the one or more portions of the blockchaintransaction 354, archiving the compressed one or more portions of theblockchain transaction 356, and storing a reference indicator in theblockchain referencing the compressed one or more portions of theblockchain transaction 358. In this example, as a transaction isidentified as being linked to a large file size or file sizes, thetransaction is separated from the files/content and written to theblockchain with a reference to a remote location where the files/contentare stored. A large file, such as a video or other large file type maybe referenced by the blockchain but not stored in the blockchain. Also,the large file(s) may be compressed and retrieved when needed during atransaction access operation.

The above embodiments may be implemented in hardware, in a computerprogram executed by a processor, in firmware, or in a combination of theabove. A computer program may be embodied on a computer readable medium,such as a storage medium. For example, a computer program may reside inrandom access memory (“RAM”), flash memory, read-only memory (“ROM”),erasable programmable read-only memory (“EPROM”), electrically erasableprogrammable read-only memory (“EEPROM”), registers, hard disk, aremovable disk, a compact disk read-only memory (“CD-ROM”), or any otherform of storage medium known in the art.

An exemplary storage medium may be coupled to the processor such thatthe processor may read information from, and write information to, thestorage medium. In the alternative, the storage medium may be integralto the processor. The processor and the storage medium may reside in anapplication specific integrated circuit (“ASIC”). In the alternative,the processor and the storage medium may reside as discrete components.For example, FIG. 4 illustrates an example network element 400, whichmay represent or be integrated in any of the above-described components,etc.

As illustrated in FIG. 4, a memory 410 and a processor 420 may bediscrete components of a network entity 400 that are used to execute anapplication or set of operations as described herein. The applicationmay be coded in software in a computer language understood by theprocessor 420, and stored in a computer readable medium, such as, amemory 410. The computer readable medium may be a non-transitorycomputer readable medium that includes tangible hardware components,such as memory, that can store software. Furthermore, a software module430 may be another discrete entity that is part of the network entity400, and which contains software instructions that may be executed bythe processor 420 to effectuate one or more of the functions describedherein. In addition to the above noted components of the network entity400, the network entity 400 may also have a transmitter and receiverpair configured to receive and transmit communication signals (notshown).

Although an exemplary embodiment of at least one of a system, method,and non-transitory computer readable medium has been illustrated in theaccompanied drawings and described in the foregoing detaileddescription, it will be understood that the application is not limitedto the embodiments disclosed, but is capable of numerous rearrangements,modifications, and substitutions as set forth and defined by thefollowing claims. For example, the capabilities of the system of thevarious figures can be performed by one or more of the modules orcomponents described herein or in a distributed architecture and mayinclude a transmitter, receiver or pair of both. For example, all orpart of the functionality performed by the individual modules, may beperformed by one or more of these modules. Further, the functionalitydescribed herein may be performed at various times and in relation tovarious events, internal or external to the modules or components. Also,the information sent between various modules can be sent between themodules via at least one of: a data network, the Internet, a voicenetwork, an Internet Protocol network, a wireless device, a wired deviceand/or via plurality of protocols. Also, the messages sent or receivedby any of the modules may be sent or received directly and/or via one ormore of the other modules.

One skilled in the art will appreciate that a “system” could be embodiedas a personal computer, a server, a console, a personal digitalassistant (PDA), a cell phone, a tablet computing device, a smartphoneor any other suitable computing device, or combination of devices.Presenting the above-described functions as being performed by a“system” is not intended to limit the scope of the present applicationin any way, but is intended to provide one example of many embodiments.Indeed, methods, systems and apparatuses disclosed herein may beimplemented in localized and distributed forms consistent with computingtechnology.

It should be noted that some of the system features described in thisspecification have been presented as modules, in order to moreparticularly emphasize their implementation independence. For example, amodule may be implemented as a hardware circuit comprising custom verylarge scale integration (VLSI) circuits or gate arrays, off-the-shelfsemiconductors such as logic chips, transistors, or other discretecomponents. A module may also be implemented in programmable hardwaredevices such as field programmable gate arrays, programmable arraylogic, programmable logic devices, graphics processing units, or thelike.

A module may also be at least partially implemented in software forexecution by various types of processors. An identified unit ofexecutable code may, for instance, comprise one or more physical orlogical blocks of computer instructions that may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified module need not be physically locatedtogether, but may comprise disparate instructions stored in differentlocations which, when joined logically together, comprise the module andachieve the stated purpose for the module. Further, modules may bestored on a computer-readable medium, which may be, for instance, a harddisk drive, flash device, random access memory (RAM), tape, or any othersuch medium used to store data.

Indeed, a module of executable code could be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set, or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork.

It will be readily understood that the components of the application, asgenerally described and illustrated in the figures herein, may bearranged and designed in a wide variety of different configurations.Thus, the detailed description of the embodiments is not intended tolimit the scope of the application as claimed, but is merelyrepresentative of selected embodiments of the application.

One having ordinary skill in the art will readily understand that theabove may be practiced with steps in a different order, and/or withhardware elements in configurations that are different than those whichare disclosed. Therefore, although the application has been describedbased upon these preferred embodiments, it would be apparent to those ofskill in the art that certain modifications, variations, and alternativeconstructions would be apparent.

While preferred embodiments of the present application have beendescribed, it is to be understood that the embodiments described areillustrative only and the scope of the application is to be definedsolely by the appended claims when considered with a full range ofequivalents and modifications (e.g., protocols, hardware devices,software platforms etc.) thereto.

What is claimed is:
 1. A method, comprising: identifying a frequency ofaccess of one or more portions of a blockchain; determining the one ormore portions of the blockchain which are eligible for archiving basedon the frequency of access of the one or more portions of theblockchain; and compressing and archiving the one or more portions ofthe blockchain eligible for archiving.
 2. The method of claim 1, furthercomprising determining a maximum data size threshold of the blockchain.3. The method of claim 1, further comprising: identifying a clientprofile has withdrawn enrollment from the blockchain; and identifying aplurality of client profile transactions logged in the blockchaincorresponding to the client profile.
 4. The method of claim 3, furthercomprising archiving the plurality of client profile transactionscorresponding to the client profile.
 5. The method of claim 4, furthercomprising: identifying the client profile has re-enrolled with theblockchain; identifying the plurality of client profile transactionscorresponding to the client profile; and restoring the plurality ofclient profile transactions responsive to the client profile beingre-enrolled with the blockchain.
 6. The method of claim 2, furthercomprising: determining the blockchain data stored in the blockchain hasreached the maximum data size threshold; and responsive to determiningthe blockchain data has reached the maximum data size threshold,archiving least frequently accessed blockchain transactions.
 7. Themethod of claim 2, further comprising: storing blockchain transactionsof the blockchain in a priority queue with the least frequently usedtransactions at respective topmost positions of the queue; and when theblockchain reaches the maximum data size threshold, archiving the leastfrequently accessed blockchain transactions at the respective topmostpositions of the queue.
 8. An apparatus, comprising: a processorconfigured to: identify a frequency of access of one or more portions ofa blockchain; determine the one or more portions of the blockchain whichare eligible to be archived based on the frequency of access of the oneor more portions of the blockchain; and compress and archive the one ormore portions of the blockchain eligible to be archived.
 9. Theapparatus of claim 8, wherein the processor is further configured todetermine a maximum data size threshold of the blockchain.
 10. Theapparatus of claim 8, wherein the processor is further configured to:identify a client profile has withdrawn enrollment from the blockchain;and identify a plurality of client profile transactions logged in theblockchain that correspond to the client profile.
 11. The apparatus ofclaim 10, wherein the processor is further configured to archive theplurality of client profile transactions that correspond to the clientprofile.
 12. The apparatus of claim 11, wherein the processor is furtherconfigured to: identify the client profile has re-enrolled with theblockchain; identify the plurality of client profile transactions thatcorrespond to the client profile; and restore the plurality of clientprofile transactions responsive to the client profile being re-enrolledwith the blockchain.
 13. The apparatus of claim 9, wherein the processoris further configured to: determine the blockchain data stored in theblockchain has reached the maximum data size threshold; and responsiveto the blockchain data determined to have reached the maximum data sizethreshold, archive least frequently accessed blockchain transactions.14. The apparatus of claim 9, wherein the processor is furtherconfigured to: store blockchain transactions of the blockchain in apriority queue with the least frequently used transactions at respectivetopmost positions of the queue; and when the blockchain reaches themaximum data size threshold, archive the least frequently accessedblockchain transactions at the respective topmost positions of thequeue.
 15. A non-transitory computer readable storage medium configuredto store instructions that when executed cause a processor to perform:identifying a frequency of access of one or more portions of ablockchain; determining the one or more portions of the blockchain whichare eligible for archiving based on the frequency of access of the oneor more portions of the blockchain; and compressing and archiving theone or more portions of the blockchain eligible for archiving.
 16. Thenon-transitory computer readable storage medium of claim 15, wherein theprocessor is further configured to perform: determining a maximum datasize threshold of the blockchain.
 17. The non-transitory computerreadable storage medium of claim 15, wherein the processor is furtherconfigured to perform: identifying a client profile has withdrawnenrollment from the blockchain; and identifying a plurality of clientprofile transactions logged in the blockchain corresponding to theclient profile.
 18. The non-transitory computer readable storage mediumof claim 17, wherein the processor is further configured to perform:archiving the plurality of client profile transactions corresponding tothe client profile.
 19. The non-transitory computer readable storagemedium of claim 18, wherein the processor is further configured toperform: identifying the client profile has re-enrolled with theblockchain; identifying the plurality of client profile transactionscorresponding to the client profile; and restoring the plurality ofclient profile transactions responsive to the client profile beingre-enrolled with the blockchain.
 20. The non-transitory computerreadable storage medium of claim 16, wherein the processor is furtherconfigured to perform: determining the blockchain data stored in theblockchain has reached the maximum data size threshold; responsive todetermining the blockchain data has reached the maximum data sizethreshold, archiving least frequently accessed blockchain transactions;storing blockchain transactions of the blockchain in a priority queuewith the least frequently used transactions at respective topmostpositions of the queue; and when the blockchain reaches the maximum datasize threshold, archiving the least frequently accessed blockchaintransactions at the respective topmost positions of the queue.